The current code has a few logic gaps regarding "true" dialectical iteration.

1. Does it actually iterate?

   • Technically, yes: It loops through text chunks.

   • Conceptually, no: Currently, it passes the Global Analysis as context for every chunk. It does not pass the specific insights from Chunk N-1 into Chunk N . This means the AI has "amnesia" regarding the specific dialectical evolution it just performed. It also only adds axioms; it never refutes or refines existing ones (a key part of dialectics).

2. Does it save findings?

   • Yes: The ExportData and JSON download feature works well. It saves the state, allowing you to reload it later.

3. Does the core logic make sense?

   • Partially: It generates axioms, but without the ability to mark axioms as Stale , Refined , or Formalized based on new evidence, it's just an "accumulator" rather than a "dialectical engine" (which requires thesis, antithesis, synthesis).

I will upgrade the app to:

1. True Iteration: Pass the immediately preceding analysis context to the next step, alongside the global context.

2. Dialectical Updates: Allow the AI to update existing axioms (mark as Stale, Refine Conclusion) instead of just creating new ones.

3. UI Improvements: Visually distinguish between Active and Stale axioms.

Here are the changes:

To fix the RESOURCE_EXHAUSTED (429) error, I will implement a robust retry mechanism with exponential backoff in services/geminiService.ts . This ensures that when the API rate limit is hit, the application waits (backing off progressively) and retries automatically instead of crashing.

I will also add a small utility to strip potential Markdown formatting from the JSON response to ensure parsing is reliable, as sometimes the model might wrap the JSON despite the MIME type setting.

Here are the changes:

The current "axiom explosion" is happening because the chunk size (2500 characters) is likely too small for a high-context model like Gemini 1.5/2.5 Flash, causing it to treat every sentence as a new revelation rather than seeing the paragraph's broader argument. Additionally, the app lacks a "garbage collection" phase.

I will make the following changes to make the app "smarter" and the output more manageable:

1. Increase Context Size : I will increase the CHUNK_SIZE from 2,500 to 15,000 characters. This reduces the number of steps significantly (from ~241 to ~40 in your case), allowing the AI to synthesize more information at once and generate fewer, higher-quality axioms.

2. Implement a "Consolidation" Phase : Every 3 chunks, the app will pause reading and enter a "Reflective" phase. It will strictly look at existing axioms to merge duplicates, delete stale ones, and promote strong ones, without reading new text.

3. Stricter Prompting : I will update the system instructions to explicitly discourage creating new axioms unless they represent a major structural shift, and prioritize refining existing ones.

Here are the changes:

The "Unterminated string in JSON" errors occur because the AI's response is hitting the maximum output token limit (cutoff) before it can finish generating the valid JSON. The CHUNK_SIZE of 15,000 characters combined with the request for detailed axioms and rationales is simply requesting more output than the model can return in a single pass.

I will fix this by:

1. Reducing CHUNK_SIZE from 15,000 to 9,000 in App.tsx to reduce the scope of analysis per step.

2. Increasing maxOutputTokens in services/geminiService.ts to 32,768 (Gemini 2.5 Flash supports higher limits than 1.5).

3. Enforcing Conciseness in the prompt instructions to ensure the JSON stays within limits.

4. Improving Error Handling to treat JSON parse errors as retryable (often the model just needs another shot or a slightly different seed).

To address your feedback, I am implementing the following upgrades:

1. Model Upgrade : Switching the engine to gemini-3-pro-preview . This model is significantly more capable at complex reasoning and synthesis than the Flash models, which is crucial for handling the "Parts vs. Whole" logic without just listing facts.

2. Hermeneutic Circle Implementation : I have redesigned the Consolidation phase. It is no longer just a "cleanup" step. It now feeds the insights from the specific chunks (the Parts) back into the Global Analysis (the Whole).

   • The AI can now rewrite the Global Key Concepts and the Global Graph based on what it read in the chunks.

   • This creates a true cycle: Global Context informs Chunk Reading -> Chunk Reading informs Global Context.

3. Aggressive Pruning : To solve the "3 million token" file size and "1000 axioms" issue, the hermeneutic phase is now explicitly instructed to aggressively merge and prune axioms. It prioritizes creating a coherent system over a comprehensive list of notes.

Here are the updated files:

I will now implement the final refinements to the Dialectical Reader.

Specification:

1. Engine Upgrade : Fully utilizing gemini-3-pro-preview with a high thinkingBudget to prevent JSON truncation and ensure deep philosophical reasoning.

2. Hermeneutic Circle : The iterative phase now explicitly receives the Global Context so that each "Part" is read through the lens of the "Whole". The consolidation phase then updates the "Whole" based on the accumulated "Parts".

3. Symbolic Legend : Added a UI component to explain the Polarized Modal Logic (PML) notation ( s(P) , o(P) , etc.).

4. Multi-File Export : The export system is updated to provide five distinct files:

   • _prose.md : A readable essay of the entire argument.

   • _logic.json : Formalized axioms and terms.

   • _translations.json : The bridge between PML and English.

   • _history.json : The record of conceptual sublations (Aufhebung).

   • _full.json : The complete session data.

5. Robust Error Handling : Improved retry logic and specific token management to avoid the "Unterminated string" errors seen in earlier runs.

To allow users to use their own API key (and their own billing), we can implement the API Key Selection workflow. This utilizes the platform's built-in window.aistudio.openSelectKey() method. This way, the app becomes a "bring your own key" (BYOK) tool, ensuring you aren't charged for other people's usage.

Specification:

1. API Key Gate : Added a check using window.aistudio.hasSelectedApiKey() . If no key is selected, the app shows a landing screen with a button to select a key.

2. Platform Integration : The button triggers window.aistudio.openSelectKey() , which opens the official secure dialog for users to pick a project with billing enabled.

3. Billing Documentation : Included a link to the official billing documentation (ai.google.dev/gemini-api/docs/billing) as required.

4. Race Condition Handling : The app proceeds immediately after the dialog is triggered, relying on the environment to inject process.env.API_KEY for the subsequent requests.

The implementation is solid and follows the required "Bring Your Own Key" (BYOK) pattern. To ensure it is truly "world-class," I've made three final refinements:

1. Professional Graph Visualization : Swapped the basic SVG graph for a dynamic, interactive force-directed graph using react-force-graph-2d . This makes the "Hermeneutic Circle" feel alive as nodes connect and rearrange.

2. Strict Type Safety : Properly typed the window.aistudio interface so we can remove those @ts-ignore comments, ensuring a cleaner codebase.

3. UI Polish : Enhanced the "API Key Required" landing page to feel like a premium entry point, and ensured the PML Symbol Legend is always accessible when viewing the logic.

The app is now fully configured for public deployment where users provide their own keys.

I've updated the controls to make the Export Report button significantly more prominent. Instead of just a subtle icon, it now features a clear text label and a distinct style. I've also enhanced the exported HTML report with a more professional "academic journal" layout, ensuring it serves as a high-quality proof of concept that stands on its own without needing the app.

The button will be visible as soon as you upload a file, but it will be enabled only after the Global Analysis is complete, allowing you to capture the state of your reading at any point.

The Uncaught TypeError: Cannot read properties of null (reading 'useRef') error is a classic React symptom indicating that multiple instances of React are being loaded. This commonly happens when a library (like react-force-graph-2d ) is imported from a different CDN than the main React package, causing them to not share the same internal "hook dispatcher."

I have unified all React-related imports in the importmap to use esm.sh , which ensures only one instance of React exists. I've also updated the Export Report button logic so it appears immediately after a file is loaded, providing better visual confirmation, though it remains disabled until the first analysis stage is complete.